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Welcome to the Big Data Exploration MCQs Page

Dive deep into the fascinating world of Big Data Exploration with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Big Data Exploration, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Big Data Exploration, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Big Data Computing.

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Check out the MCQs below to embark on an enriching journey through Big Data Exploration. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Big Data Computing.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Big Data Exploration. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Big Data Exploration MCQs | Page 8 of 9

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Q71.
Why does NoDB prioritize caching integer attributes over string attributes?

a.

Integer attributes are more frequently used in queries.

b.

Fetching integer attributes from the raw file is less expensive than fetching string attributes.

c.

String attributes require less parsing during caching.

d.

String attributes have higher priority for cache replacement.

Discuss
Answer: (b).Fetching integer attributes from the raw file is less expensive than fetching string attributes. Explanation:NoDB prioritizes caching integer attributes over string attributes because fetching integer attributes from the raw file is less expensive in terms of parsing costs compared to fetching string attributes.
Q72.
How does NoDB handle the generation of statistics when using adaptive loading?

a.

Statistics are generated for all attributes during system initialization.

b.

Statistics are precomputed and stored in a separate file.

c.

Statistics are calculated on-the-fly the first time an attribute is requested by a query.

d.

No statistics are generated, and queries rely on heuristics for optimization.

Discuss
Answer: (c).Statistics are calculated on-the-fly the first time an attribute is requested by a query. Explanation:In NoDB, statistics are calculated on-the-fly the very first time an attribute of a given raw file is requested by a query. This approach allows the system to avoid bad optimization choices.
Q73.
What is the purpose of NoDB's text cracking?

a.

To optimize the storage of raw files.

b.

To separate data into multiple files based on query predicates.

c.

To reduce the amount of data that needs to be touched during queries by splitting raw files.

d.

To compress raw files for efficient storage.

Discuss
Answer: (c).To reduce the amount of data that needs to be touched during queries by splitting raw files. Explanation:The purpose of NoDB's text cracking is to reduce the amount of data that needs to be touched during queries by splitting raw files into smaller files, allowing queries to work on smaller portions of the data.
Q74.
What is the main benefit of the data vaults project in the context of scientific data management?

a.

It transforms scientific data into a database format.

b.

It eliminates all parsing and tokenizing costs.

c.

It loads only the metadata during initialization, reducing setup costs.

d.

It provides indexing information for scientific data formats.

Discuss
Answer: (c).It loads only the metadata during initialization, reducing setup costs. Explanation:The main benefit of the data vaults project in the context of scientific data management is that it loads only the metadata during initialization, resulting in a minimal setup cost.
Q75.
In the data vaults project, what is used to guide queries to the proper files and transform only the needed data on-the-fly?

a.

Metadata information

b.

Parsing and tokenizing algorithms

c.

Indexing information

d.

Clustering data

Discuss
Answer: (a).Metadata information Explanation:In the data vaults project, metadata information is used to guide queries to the proper files and transform only the needed data on-the-fly during query processing.
Q76.
What does adaptive loading aim to achieve in the context of data management?

a.

To eliminate data loading costs entirely

b.

To load all data into memory during system initialization

c.

To transform all data into a database format

d.

To make systems usable as soon as data arrive by reducing loading costs

Discuss
Answer: (d).To make systems usable as soon as data arrive by reducing loading costs Explanation:Adaptive loading aims to make systems usable as soon as data arrive by reducing loading costs, allowing immediate access to data without the need for extensive loading operations.
Q77.
What is the main idea behind the Sciborg approach to query processing?

a.

To ensure correctness and completeness in query results

b.

To perform queries over a hierarchy of samples for improved response times

c.

To use random samples for query processing

d.

To organize data in a hierarchical structure for better storage efficiency

Discuss
Answer: (b).To perform queries over a hierarchy of samples for improved response times Explanation:The main idea behind the Sciborg approach to query processing is to perform queries over a hierarchy of samples, which allows for improved response times, especially in interactive query scenarios.
Q78.
In Sciborg, how are samples of data created and organized?

a.

Randomly

b.

Based on hierarchical data structures

c.

Using past query-processing actions and data properties

d.

According to a predefined sampling algorithm

Discuss
Answer: (c).Using past query-processing actions and data properties Explanation:In Sciborg, samples of data are created and organized based on past query-processing actions and the properties of the data, rather than being random samples.
Q79.
What is the primary goal of the Sciborg approach to query processing?

a.

To ensure correctness and completeness of query results

b.

To reduce response times at the cost of correctness and completeness

c.

To create a hierarchical structure for data storage

d.

To improve storage efficiency

Discuss
Answer: (b).To reduce response times at the cost of correctness and completeness Explanation:The primary goal of the Sciborg approach to query processing is to reduce response times at the cost of sacrificing correctness and completeness of query results, especially in interactive query scenarios.
Q80.
What is the main benefit of the Sciborg approach to query processing?

a.

It guarantees correctness and completeness in query results.

b.

It provides a hierarchical structure for efficient data storage.

c.

It offers improved response times for interactive queries.

d.

It focuses on random sampling for query processing.

Discuss
Answer: (c).It offers improved response times for interactive queries. Explanation:The main benefit of the Sciborg approach to query processing is that it offers improved response times, particularly for interactive queries, by allowing queries to be performed over a hierarchy of samples.
Page 8 of 9

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